| Literature DB >> 35463000 |
Silvia De Rosa1, Marita Marengo2, Stefano Romagnoli3, Marco Fiorentino4, Vito Fanelli5, Enrico Fiaccadori6, Nicola Brienza7, Santo Morabito8, Vincenzo Pota9, Fabrizio Valente10, Giacomo Grasselli11, Piergiorgio Messa12, Antonino Giarratano13, Vincenzo Cantaluppi14.
Abstract
Background and Aim: The novel coronavirus disease 2019 remains challenging. A large number of hospitalized patients are at a high risk of developing AKI. For this reason, we conducted a nationwide survey to assess the incidence and management of AKI in critically ill patients affected by the SARS-CoV-2 infection.Entities:
Keywords: COVID-19; acute kidney injury; blood purification; critical care; renal replacement therapy; surveys and questionnaires
Year: 2022 PMID: 35463000 PMCID: PMC9021595 DOI: 10.3389/fmed.2022.850535
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
Figure 1Acute kidney injury. (A) Shows the percentage of underestimation of acute kidney injury (AKI) referred only to patients who needed RRT. (B) Shows the use of AKI definition criteria (AKIN, KDIGO, RIFLE, etc.) to classify AKI in patients with COVID-19. (C) Shows the use of urinalysis or biomarkers for AKI in patients with COVID-19.
Figure 2Renal replacement therapies: indication, modality, timing, and prone positioning influence. (A) Shows the percentage of parameters used to define the indication to start RRT. (B) Shows the percentage of early vs. late initiation of RRT. (C) Shows the percentage of RRTs for AKI. (D) Shows the percentage influencing the choice of dialytic strategy due to prone positioning.
Figure 3Anticoagulation strategies for RRTs during COVID-19 pandemic. The chart showed the absolute number of different anticoagulation strategies for the extracorporeal circuit in patients with COVID-19.
Figure 4Extracorporeal blood purification therapies. (A) Reported the conditions considered to start extracorporeal blood purification therapies. (B) Reported the membranes or modalities used in patients with COVID-19. (C) Reported the likelihood to test interleukin-6 (IL-6) in enrolled centers.
Baseline characteristics.
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| Male, n (%) | 87 (62) | 45 (45) | 42 (43) |
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| <5 | 12 (6) | 9 (11) | 4 (7) |
| <10 | 14 (10) | 10 (10) | 4 (6) |
| <15 | 15 (11) | 8 (13) | 7 (11) |
| > 15 | 90 (64) | 42 (54) | 48 (76) |
| Residents | 9 (9) | 9 (12) | 0 (0) |
| Hospital COVID-19 beds | 80 (0–650) | 70 (0–400) | 120 (0–650) |
| Semi- intensive COVID-19 beds | 15 (0–134) | 10 (0–90) | 20 (0–134) |
| Intensive COVID-19 beds | 17 (0–400) | 16 (0–150) | 20 (0–400) |
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| Abruzzo | 2 (2) | 2 (3) | 0 (0) |
| Basilicata | 1 (1) | 1 (1) | 0 (0) |
| Calabria | 6 (4) | 6 (8) | 0 (0) |
| Campania | 7 (5) | 5 (6) | 2 (3) |
| Città del Vaticano | 0 (0) | 0 (0) | 0 (0) |
| Emilia-Romagna | 10 (7) | 5 (6) | 5 (8) |
| Friuli-Venezia Giulia | 4 (3) | 3 (4) | 1 (2) |
| Lazio | 8 (5) | 4 (5) | 4 (6) |
| Liguria | 2 (2) | 1 (1) | 1 (2) |
| Lombardia | 23 (16) | 17 (22) | 6 (0.9) |
| Marche | 3(2) | 2 (3) | 1 (2) |
| Molise | 0 (0) | 0 (0) | 0 (0) |
| Piemonte | 24 (17) | 9 (12) | 15 (24) |
| Puglia | 9 (6) | 3 (4) | 6 (9) |
| Repubblica di San Marino | 0 (0) | 0 (0) | 0 (0) |
| Sardegna | 3 (2) | 1 (1) | 2 (3) |
| Sicilia | 12 (9) | 6 (8) | 6 (10) |
| Toscana | 9 (6) | 2 (3) | 7 (11) |
| Trentino-Alto Adige | 4 (3) | 2 (3) | 2 (3) |
| Umbria | 1 (1) | 1 (1) | 0 (0) |
| Valle d'Aosta | 2 (2) | 1 (1) | 1(2) |
| Veneto | 10 (7) | 6 (8) | 4 (6) |
| COVID-19 patients admitted in Hospital | 80 (0–2,500) | 69 (1–2,000) | 120 (0–2,500) |
| COVID-19 patients with AKI | 5 (0–100) | 5 (0–100) | 5 (0–40) |
| COVID-19 ICU patients with AKI | 4 (0–90) | 4 (0–90) | 4 (0–88) |